Companies use Information Technology (IT) infrastructure to execute various aspects of their businesses. Different automated services (e.g., online banking, mobile banking, ATM operations, or mobile check cashing) experience different patterns of business demand from customers. The demand fluctuations can, for example, be on an hourly, daily, weekly, monthly, quarterly, annually, or seasonal basis. Nevertheless, customers expect that the Information Technology (IT) infrastructure will be able to provide expeditious service on demand at all times. Traditionally, in industries with a complex supply chain, businesses have to rely upon partners in the lines of business to provide details around business demand. The data provided, however, tends to come in different time intervals, many times with missing or inconsistent data.
In addition to lacking consistent and accurate business demand data sources, knowing which infrastructure components support which business services may be challenging. Also, studying the demands on IT infrastructure posed by each business service may be challenging and time consuming. Further, refreshing and validating models studying IT demands may be challenging given the lack of a baseline for comparison due to rapidly changing demand and business conditions.
Currently, time-series charts are used to predict when servers and databases may run out of capacity, and to resolve issues. Time-series trending, however, comes without sufficient context. For example, upon identifying that a server's utilization increased from 40 to 50% without any other context suggests that there could be a problem, whereas an accompanying context that a marketing campaign caused a temporary spike in demand for a service facilitated by that server would indicate otherwise. Currently, business-driven capacity models are developed on a case-by-case basis, manually. The development of automated models is limited, however, by technological and computational constraints in performing the modeling.